The long-term goal of the Strong Heart Family Study is to detect, map, and identify polymorphic genes that influence variation in risk factors for cardiovascular disease (CVD) and other related disorders that are major health problems in American Indians. Of immediate interest are risk factors and precursors such as lipid phenotypes, diabetes phenotypes, measures of obesity, measures associated with hemostasis, and measures of cardiac and arterial structure and function. In Phases III and IV of the Strong Heart Study (SHS), we have collaborated with the other SHS investigators in successfully recruiting and examining more than 1,200 members of extended families in each of three centers (in Arizona, Oklahoma, and the Dakotas). We have shown that many of the CVD-related phenotypes are heritable, and we will soon complete a 10centimorgan map that includes genotypes for 386 short tandem repeats (STRS) in each of the 3,600+individuals. In linkage screening of as many as 2,100 individuals, we have identified numerous chromosomal regions containing promising linkage signals: left ventricular mass normalized for height (chr 12p, LOD=5.3);weight (LOD=5.17) and body mass index (LOD=5.08) (chr 4q);plasma insulin level (LOD=3.5) and lean body mass (LOD=2.6) (chr 2p);ejection fraction (chr 1q, LOD=3.5);LDL-C (chr 10p, LOD=3.7);PAI-1 (chr 11p,LOD=3.03);and clusters of insulin resistance syndrome variables identified by factor analysis including factors for glucose/insulin/obesity (chr 4, LOD=2.2) and dyslipidemia (chr 12, LOD=2.7). We will continue our linkage analyses and will do finer scale mapping to more precisely localize quantitative trait loci (QTLs) within targeted chromosomal regions. This will involve prioritizing of candidate genes and extensive sequencing of multiple candidate genes within the region of each QTL to identify single nucleotide polymorphisms (SNPs), and then SNP typing of all SNPs that we identify in candidate genes. Our analyses will enable us to test whether specific SNP polymorphism(s) account for our linkage signals. We also will test whether the SNPs account for population-level association in the SHS cohort. The use of new Bayesian methods will enable us to distinguish functional genes from polymorphisms in linkage disequilibrium with them and will greatly reduce the amount of time-consuming molecular genetic analysis that would otherwise be necessary to narrow in on functional polymorphisms. (End of Abstract)